Map indicates the layout of the F1 generation resulting from a cross between EC201 and EC103 parents. Column 1 is approximately lengthways facing north.
“Map of F1 Trees”
Diagram indicates the areas of leaf collection regarding height. Each Tree generally had 10 leaves collected (some trees have been sampled more than once), leaves were selected from low, mid and high points of the tree. The first leaf sampled from each tree was measured twice for replication comparison.
“Diagram of leaf collection levels”
“Full.xlsx”, sheet “Full” contains measurement information from sampling with the Dualex (https://www.force-a.com/en/capteurs-optiques-optical-sensors/dualex-scientific-chlorophyll-meter/), including;
Surface content of chlorophyll in \(g/cm^2\) (Chl)
Epidermal Flavoid content in absorbance units; Flavonol(Flav) and Anthocyanin(Anth)
Nitrogen Balance Index status is calculated using Chlorophyll and Flavonol values automatically (NBI)
It also contains information about the block position, the leaf height information, and presense or absence of flowering
Sheet “Dup” contains only the replicated samples
# Import Data Measures
Data <-read.xlsx("Full.xlsx", sheetName ="Full")
head(Data)
## Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 1 4 CG 0 0 0 20 OG CG N
## 2 4 CG 0 0 0 20 OG CG N
## 3 4 CG 0 0 0 20 OG CG N
## 4 4 CG 0 0 0 20 OG CG N
## 5 4 CG 0 0 0 20 OG CG N
## 6 4 CG 0 0 0 20 OG CG N
## measure Height Flower Chl Flav Anth NBI
## 1 9 H Y 28.220 2.418 0.205 11.67
## 2 3 L Y 27.958 2.298 0.691 12.17
## 3 11 H Y 33.727 2.458 0.527 13.72
## 4 4 L Y 25.938 1.758 0.172 14.76
## 5 10 H Y 36.205 2.283 0.270 15.86
## 6 6 M Y 34.332 2.115 0.150 16.23
Data$Column = as.factor(Data$Column)
Data$Row = as.factor(Data$Row)
# Import Replicate Data
Dup <-read.xlsx("Full.xlsx", sheetName ="Dup")
head(Dup)
## Collection.Day group Group.ID Tree.ID Rep. measure Height Chl Flav
## 1 2.0 23 60 IN4DV Y1 1 L 1.916 2.363
## 2 1.5 3 1 IN4BT Y1 1 L 3.124 2.300
## 3 2.0 17 54 IN4DL Y2 2 L 3.414 1.826
## 4 1.5 32 28 IN4CP Y2 2 L 4.097 1.943
## 5 2.0 9 46 IN4DC Y1 1 L 4.909 1.928
## 6 1.5 5 3 IN4BW Y1 1 L 4.924 1.848
## Anth NBI
## 1 0.174 0.81
## 2 0.191 1.36
## 3 0.112 1.87
## 4 0.051 2.11
## 5 0.103 2.55
## 6 0.165 2.66
#Isolate Crimson Glory Outgroup
CG = Data[c(1:11),]
#Isolate East Cape 201 Parent
EC201 = Data[c(12:21),]
#Isolate East Cape 103 Parent
EC103 = Data[c(22:33),]
#Isolate Offspring from the Parental Cross
F1 = Data[c(34:1825),]
Replicates were taken by re-measuring a single leaf sample taken from each tree, this was to help establish the consistency of measurements given by the Dualex to help verify measurement accuracy.
## Min. 1st Qu. Median Mean 3rd Qu.
## RepAnth 0.00100000 0.06500000 0.09850000 0.09799367 0.12525000
## RepChl 1.91600000 25.82475000 36.02700000 35.76269937 46.61000000
## RepFlav 1.05600000 1.76775000 1.99400000 1.96720570 2.19725000
## RepNBI 0.81000000 12.70250000 18.19000000 18.73398734 24.45750000
## Max.
## RepAnth 0.25400000
## RepChl 59.67400000
## RepFlav 2.71400000
## RepNBI 43.49000000
## # A tibble: 2 x 5
## Rep. Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Y1 0.0983 34.9 1.98 18.2
## 2 Y2 0.0977 36.6 1.95 19.2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
There is no statistically significant differences between the two groups of measurements, this is a good sign indicative of the accuracy of the Dualex.
## Analysis of Variance Table
##
## Response: Dup$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.00003 0.0000304 0.0151 0.9023
## Residuals 314 0.63303 0.0020160
##
## Call:
## lm(formula = Dup$Anth ~ Dup$Rep.)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.097302 -0.032682 0.000818 0.027568 0.156318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0983019 0.0035608 27.607 <2e-16 ***
## Dup$Rep.Y2 -0.0006204 0.0050518 -0.123 0.902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0449 on 314 degrees of freedom
## Multiple R-squared: 4.802e-05, Adjusted R-squared: -0.003137
## F-statistic: 0.01508 on 1 and 314 DF, p-value: 0.9023
## Analysis of Variance Table
##
## Response: Dup$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 218 218.33 1.033 0.3102
## Residuals 314 66366 211.36
##
## Call:
## lm(formula = Dup$Chl ~ Dup$Rep.)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.185 -9.724 0.252 10.891 24.737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.937 1.153 30.302 <2e-16 ***
## Dup$Rep.Y2 1.662 1.636 1.016 0.31
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.54 on 314 degrees of freedom
## Multiple R-squared: 0.003279, Adjusted R-squared: 0.0001048
## F-statistic: 1.033 on 1 and 314 DF, p-value: 0.3102
## Analysis of Variance Table
##
## Response: Dup$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 0.0521 0.052129 0.6506 0.4205
## Residuals 314 25.1596 0.080126
##
## Call:
## lm(formula = Dup$Flav ~ Dup$Rep.)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.89828 -0.19930 0.01538 0.23197 0.73403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.97997 0.02245 88.200 <2e-16 ***
## Dup$Rep.Y2 -0.02569 0.03185 -0.807 0.421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2831 on 314 degrees of freedom
## Multiple R-squared: 0.002068, Adjusted R-squared: -0.00111
## F-statistic: 0.6506 on 1 and 314 DF, p-value: 0.4205
## Analysis of Variance Table
##
## Response: Dup$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Dup$Rep. 1 82.5 82.530 1.115 0.2918
## Residuals 314 23241.0 74.016
##
## Call:
## lm(formula = Dup$NBI ~ Dup$Rep.)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.4162 -5.9683 -0.7512 5.5938 24.2417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.2262 0.6823 26.714 <2e-16 ***
## Dup$Rep.Y2 1.0221 0.9680 1.056 0.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.603 on 314 degrees of freedom
## Multiple R-squared: 0.003538, Adjusted R-squared: 0.000365
## F-statistic: 1.115 on 1 and 314 DF, p-value: 0.2918
The absence of statistically significant results indicates that our replicates are likely to be consistent.
Allocation refers to which group measurements were taken from, i.e. A Parental Tree (EC103 or EC201), Outgroup Tree (CG), Parental Offspring (F1)
## Min. 1st Qu. Median Mean 3rd Qu.
## AllAnth 0.00100000 0.06800000 0.09600000 0.09772877 0.12400000
## AllChl 0.13000000 24.10200000 36.21000000 35.21443342 46.82300000
## AllFlav 1.05600000 1.80600000 1.97800000 1.96440493 2.12800000
## AllNBI 0.07000000 12.14000000 18.64000000 18.34807123 24.44000000
## Max.
## AllAnth 0.69100000
## AllChl 59.90500000
## AllFlav 2.86100000
## AllNBI 49.17000000
## # A tibble: 4 x 5
## Allocation Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 CG 0.258 36.0 2.17 16.7
## 2 EC103 0.0588 52.6 1.60 33.1
## 3 EC201 0.0779 52.6 1.71 31.7
## 4 F1 0.0971 35.0 1.97 18.2
## Analysis of Variance Table
##
## Response: Data$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 0.3040 0.101343 47.474 < 2.2e-16 ***
## Residuals 1821 3.8873 0.002135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Data$Anth ~ Data$Allocation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15064 -0.02912 -0.00112 0.02688 0.43336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25764 0.01393 18.494 <2e-16 ***
## Data$AllocationEC103 -0.19880 0.01929 -10.308 <2e-16 ***
## Data$AllocationEC201 -0.17974 0.02019 -8.903 <2e-16 ***
## Data$AllocationF1 -0.16052 0.01397 -11.487 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0462 on 1821 degrees of freedom
## Multiple R-squared: 0.07254, Adjusted R-squared: 0.07101
## F-statistic: 47.47 on 3 and 1821 DF, p-value: < 2.2e-16
## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Data$Chl ~ Data$Allocation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.866 -10.918 1.121 11.493 24.909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.028 4.539 7.937 3.58e-15 ***
## Data$AllocationEC103 16.564 6.284 2.636 0.00847 **
## Data$AllocationEC201 16.578 6.578 2.520 0.01181 *
## Data$AllocationF1 -1.032 4.553 -0.227 0.82066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.05 on 1821 degrees of freedom
## Multiple R-squared: 0.01607, Adjusted R-squared: 0.01445
## F-statistic: 9.915 on 3 and 1821 DF, p-value: 1.748e-06
## Analysis of Variance Table
##
## Response: Data$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 2.713 0.90423 14.755 1.708e-09 ***
## Residuals 1821 111.598 0.06128
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Data$Flav ~ Data$Allocation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9110 -0.1568 0.0110 0.1610 0.8940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.16945 0.07464 29.065 < 2e-16 ***
## Data$AllocationEC103 -0.56962 0.10334 -5.512 4.05e-08 ***
## Data$AllocationEC201 -0.45865 0.10816 -4.240 2.34e-05 ***
## Data$AllocationF1 -0.20245 0.07487 -2.704 0.00691 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2476 on 1821 degrees of freedom
## Multiple R-squared: 0.02373, Adjusted R-squared: 0.02212
## F-statistic: 14.75 on 3 and 1821 DF, p-value: 1.708e-09
## Analysis of Variance Table
##
## Response: Data$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Data$Allocation 3 6742 2247.21 9.9149 1.748e-06 ***
## Residuals 1821 412728 226.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Data$NBI ~ Data$Allocation)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1144 -6.1044 0.3556 6.0656 30.9856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.739 2.540 6.591 5.72e-11 ***
## Data$AllocationEC103 16.404 3.516 4.665 3.31e-06 ***
## Data$AllocationEC201 14.949 3.681 4.062 5.08e-05 ***
## Data$AllocationF1 1.445 2.548 0.567 0.571
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.424 on 1821 degrees of freedom
## Multiple R-squared: 0.03353, Adjusted R-squared: 0.03194
## F-statistic: 21.06 on 3 and 1821 DF, p-value: 2.069e-13
F1 Samples (Approx 3 years) appear more similar to that of the Crimson Glory plant than either/both parents - this is possibly due to age effects as CG is likely more similar in this regard being shorter (No age confirmed). ANOVAs incidate there is significant differences between the allocations - this is to be expected.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## ParAnth 0.004000 0.048250 0.058000 0.067500 0.086750 0.156000
## ParChl 30.722000 50.356750 55.739000 52.598909 57.794750 59.757000
## ParFlav 1.293000 1.475000 1.594000 1.650273 1.821000 2.138000
## ParNBI 14.370000 29.872500 34.025000 32.481818 35.892500 44.880000
## # A tibble: 2 x 5
## Tree.ID Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 EC103 0.0588 52.6 1.60 33.1
## 2 EC201 0.0779 52.6 1.71 31.7
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Welch Two Sample t-test
##
## data: Parent$Anth by Parent$Tree.ID
## t = -1.2283, df = 19.107, p-value = 0.2342
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05154327 0.01340994
## sample estimates:
## mean in group EC103 mean in group EC201
## 0.05883333 0.07790000
##
## Welch Two Sample t-test
##
## data: Parent$Chl by Parent$Tree.ID
## t = -0.003842, df = 17.804, p-value = 0.997
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.830954 7.802387
## sample estimates:
## mean in group EC103 mean in group EC201
## 52.59242 52.60670
##
## Welch Two Sample t-test
##
## data: Parent$Flav by Parent$Tree.ID
## t = -1.141, df = 19.612, p-value = 0.2676
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.31409194 0.09215861
## sample estimates:
## mean in group EC103 mean in group EC201
## 1.599833 1.710800
##
## Welch Two Sample t-test
##
## data: Parent$NBI by Parent$Tree.ID
## t = 0.47573, df = 14.7, p-value = 0.6413
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.076667 7.987334
## sample estimates:
## mean in group EC103 mean in group EC201
## 33.14333 31.68800
Interesting sample pattern here, Chl and NBI start low and work high, Flav does the opposite. Maybe accuracy of measurements?
## # A tibble: 6 x 5
## Tree.ID Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 IN4BT 34.5 16.2 0.110 2.17
## 2 IN4BV 27.5 13.8 0.121 2.01
## 3 IN4BW 24.8 12.5 0.127 1.99
## 4 IN4BX 37.6 18.4 0.0905 2.05
## 5 IN4BY 24.2 12.5 0.110 1.98
## 6 IN4BZ 32.0 15.6 0.106 2.09
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 0.5253 0.0033247 1.8093 2.001e-08 ***
## Residuals 1633 3.0007 0.0018376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 47130 298.29 1.3395 0.004474 **
## Residuals 1633 363634 222.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 25.118 0.15897 3.0531 < 2.2e-16 ***
## Residuals 1633 85.030 0.05207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Tree.ID 158 16378 103.661 1.5147 8.435e-05 ***
## Residuals 1633 111761 68.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Msr = group_by(F1, Tree.ID, measure)
Msr = summarise(Msr, Anth = mean(Anth), Flav = mean(Flav), Chl = mean(Chl),NBI = mean(NBI))
#Select Random Column
#sample(1:4,10, replace = T)
#[1] 3 3 2 1 1 4 1 2 4 3
#Select Random Row
#sample(1:50,10, replace = T)
#[1] 34 37 44 20 44 47 9 19 22 40
## # A tibble: 3 x 5
## Height Anth Chl Flav NBI
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 H 0.105 33.9 1.93 17.9
## 2 L 0.0968 35.6 1.98 18.5
## 3 M 0.0913 35.3 1.98 18.1
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.0526 0.0262759 13.533 1.468e-06 ***
## Residuals 1789 3.4735 0.0019416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = F1$Anth ~ F1$Height)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10400 -0.02878 -0.00056 0.02666 0.37800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.105002 0.001973 53.232 < 2e-16 ***
## F1$HeightL -0.008225 0.002622 -3.137 0.00173 **
## F1$HeightM -0.013666 0.002630 -5.197 2.26e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04406 on 1789 degrees of freedom
## Multiple R-squared: 0.0149, Adjusted R-squared: 0.0138
## F-statistic: 13.53 on 2 and 1789 DF, p-value: 1.468e-06
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 1.8605 0.1559
## Residuals 1789 409911 229.13
##
## Call:
## lm(formula = F1$Chl ~ F1$Height)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.125 -11.129 1.178 11.731 25.998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.9073 0.6776 50.038 <2e-16 ***
## F1$HeightL 1.6673 0.9006 1.851 0.0643 .
## F1$HeightM 1.3481 0.9034 1.492 0.1358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.14 on 1789 degrees of freedom
## Multiple R-squared: 0.002076, Adjusted R-squared: 0.00096
## F-statistic: 1.861 on 2 and 1789 DF, p-value: 0.1559
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.915 0.45758 7.4943 0.0005739 ***
## Residuals 1789 109.232 0.06106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = F1$Flav ~ F1$Height)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9225 -0.1490 0.0170 0.1655 0.8775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.93079 0.01106 174.548 < 2e-16 ***
## F1$HeightL 0.04770 0.01470 3.245 0.00120 **
## F1$HeightM 0.05272 0.01475 3.575 0.00036 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2471 on 1789 degrees of freedom
## Multiple R-squared: 0.008309, Adjusted R-squared: 0.0072
## F-statistic: 7.494 on 2 and 1789 DF, p-value: 0.0005739
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 100 50.044 0.6992 0.4971
## Residuals 1789 128039 71.570
##
## Call:
## lm(formula = F1$NBI ~ F1$Height)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.0776 -6.1206 0.3474 6.0613 31.0224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.8703 0.3787 47.186 <2e-16 ***
## F1$HeightL 0.5912 0.5034 1.175 0.240
## F1$HeightM 0.2772 0.5049 0.549 0.583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.46 on 1789 degrees of freedom
## Multiple R-squared: 0.0007811, Adjusted R-squared: -0.000336
## F-statistic: 0.6992 on 2 and 1789 DF, p-value: 0.4971
## # A tibble: 6 x 6
## # Groups: Height [1]
## Height Tree.ID Anth Chl Flav NBI
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 H IN4BT 0.116 43.6 2.09 21.1
## 2 H IN4BV 0.104 28.1 1.93 14.7
## 3 H IN4BW 0.12 18.5 2.06 9.15
## 4 H IN4BX 0.0875 43.2 2.13 20.3
## 5 H IN4BY 0.121 27.7 1.96 14.3
## 6 H IN4BZ 0.0817 39.1 2.10 18.5
## Analysis of Variance Table
##
## Response: High$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Anth 1 0.009644 0.0096440 10.431 0.00151 **
## Residuals 157 0.145162 0.0009246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = High$Anth ~ Low$Anth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.082724 -0.020196 -0.001244 0.020111 0.095669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075038 0.009659 7.769 9.67e-13 ***
## Low$Anth 0.312539 0.096773 3.230 0.00151 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03041 on 157 degrees of freedom
## Multiple R-squared: 0.0623, Adjusted R-squared: 0.05632
## F-statistic: 10.43 on 1 and 157 DF, p-value: 0.00151
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Mid$Anth 1 0.001607 0.00160739 1.6473 0.2012
## Residuals 157 0.153198 0.00097579
##
## Call:
## lm(formula = High$Anth ~ Mid$Anth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080176 -0.019699 -0.001994 0.017792 0.120025
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09193 0.01067 8.616 6.97e-15 ***
## Mid$Anth 0.14627 0.11397 1.283 0.201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03124 on 157 degrees of freedom
## Multiple R-squared: 0.01038, Adjusted R-squared: 0.00408
## F-statistic: 1.647 on 1 and 157 DF, p-value: 0.2012
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: Mid$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Anth 1 0.002597 0.00259684 5.6212 0.01896 *
## Residuals 157 0.072529 0.00046197
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Mid$Anth ~ Low$Anth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074218 -0.012195 -0.000834 0.013593 0.058269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075378 0.006828 11.040 <2e-16 ***
## Low$Anth 0.162181 0.068404 2.371 0.019 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02149 on 157 degrees of freedom
## Multiple R-squared: 0.03457, Adjusted R-squared: 0.02842
## F-statistic: 5.621 on 1 and 157 DF, p-value: 0.01896
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Chl 1 40.6 40.577 0.4215 0.5171
## Residuals 157 15112.7 96.259
##
## Call:
## lm(formula = High$Chl ~ Low$Chl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.4732 -6.5662 0.8979 6.6457 24.2522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.03022 3.39455 10.614 <2e-16 ***
## Low$Chl -0.05995 0.09234 -0.649 0.517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.811 on 157 degrees of freedom
## Multiple R-squared: 0.002678, Adjusted R-squared: -0.003675
## F-statistic: 0.4215 on 1 and 157 DF, p-value: 0.5171
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Mid$Chl 1 6.5 6.517 0.0676 0.7953
## Residuals 157 15146.8 96.476
##
## Call:
## lm(formula = High$Chl ~ Mid$Chl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.3314 -6.5596 0.9516 7.1975 23.9071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.75595 3.44048 10.10 <2e-16 ***
## Mid$Chl -0.02468 0.09494 -0.26 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.822 on 157 degrees of freedom
## Multiple R-squared: 0.0004301, Adjusted R-squared: -0.005937
## F-statistic: 0.06755 on 1 and 157 DF, p-value: 0.7953
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: Mid$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Chl 1 86.5 86.465 1.2786 0.2599
## Residuals 157 10617.2 67.625
##
## Call:
## lm(formula = Mid$Chl ~ Low$Chl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.2237 -4.2267 0.5812 5.1386 17.4487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.16619 2.84521 11.305 <2e-16 ***
## Low$Chl 0.08752 0.07740 1.131 0.26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.223 on 157 degrees of freedom
## Multiple R-squared: 0.008078, Adjusted R-squared: 0.00176
## F-statistic: 1.279 on 1 and 157 DF, p-value: 0.2599
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Flav 1 0.15207 0.152067 7.9384 0.005462 **
## Residuals 157 3.00745 0.019156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = High$Flav ~ Low$Flav)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69197 -0.07612 0.01771 0.09061 0.31947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.59267 0.11926 13.355 < 2e-16 ***
## Low$Flav 0.16916 0.06004 2.818 0.00546 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1384 on 157 degrees of freedom
## Multiple R-squared: 0.04813, Adjusted R-squared: 0.04207
## F-statistic: 7.938 on 1 and 157 DF, p-value: 0.005462
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Mid$Flav 1 0.36919 0.36919 20.773 1.034e-05 ***
## Residuals 157 2.79032 0.01777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = High$Flav ~ Mid$Flav)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69325 -0.06998 0.01107 0.09397 0.33382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.30244 0.13749 9.473 < 2e-16 ***
## Mid$Flav 0.31517 0.06915 4.558 1.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1333 on 157 degrees of freedom
## Multiple R-squared: 0.1169, Adjusted R-squared: 0.1112
## F-statistic: 20.77 on 1 and 157 DF, p-value: 1.034e-05
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: Mid$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$Flav 1 0.5438 0.54376 26.906 6.506e-07 ***
## Residuals 157 3.1729 0.02021
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Mid$Flav ~ Low$Flav)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.42522 -0.08825 0.00511 0.08340 0.47765
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.34975 0.12249 11.019 < 2e-16 ***
## Low$Flav 0.31987 0.06167 5.187 6.51e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1422 on 157 degrees of freedom
## Multiple R-squared: 0.1463, Adjusted R-squared: 0.1409
## F-statistic: 26.91 on 1 and 157 DF, p-value: 6.506e-07
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$NBI 1 12.2 12.156 0.4216 0.5171
## Residuals 157 4527.1 28.835
##
## Call:
## lm(formula = High$NBI ~ Low$NBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.3802 -3.4352 -0.0889 3.7378 11.3144
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.88822 1.61024 11.730 <2e-16 ***
## Low$NBI -0.05431 0.08365 -0.649 0.517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.37 on 157 degrees of freedom
## Multiple R-squared: 0.002678, Adjusted R-squared: -0.003674
## F-statistic: 0.4216 on 1 and 157 DF, p-value: 0.5171
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: High$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Mid$NBI 1 5.6 5.6094 0.1943 0.66
## Residuals 157 4533.7 28.8769
##
## Call:
## lm(formula = High$NBI ~ Mid$NBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.1320 -3.5765 0.0304 4.0196 11.0499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.61881 1.72974 10.764 <2e-16 ***
## Mid$NBI -0.04062 0.09217 -0.441 0.66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.374 on 157 degrees of freedom
## Multiple R-squared: 0.001236, Adjusted R-squared: -0.005126
## F-statistic: 0.1943 on 1 and 157 DF, p-value: 0.66
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: Mid$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## Low$NBI 1 106.0 105.990 5.0525 0.02598 *
## Residuals 157 3293.5 20.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Mid$NBI ~ Low$NBI)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.0884 -2.6169 -0.0763 2.5932 11.7726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.21189 1.37343 11.076 <2e-16 ***
## Low$NBI 0.16038 0.07135 2.248 0.026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.58 on 157 degrees of freedom
## Multiple R-squared: 0.03118, Adjusted R-squared: 0.02501
## F-statistic: 5.053 on 1 and 157 DF, p-value: 0.02598
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## # A tibble: 6 x 5
## Row Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 35.6 17.3 0.107 2.09
## 2 10 30.5 15.7 0.112 1.98
## 3 11 29.9 15.0 0.112 2.04
## 4 12 34.6 18.7 0.0968 1.84
## 5 13 31.3 17.5 0.107 1.86
## 6 14 35.0 17.9 0.0888 1.98
## Row Chl NBI Anth
## 17 :1 Min. :32.13 Min. :17.14 Min. :0.1163
## 0 :0 1st Qu.:32.13 1st Qu.:17.14 1st Qu.:0.1163
## 1 :0 Median :32.13 Median :17.14 Median :0.1163
## 10 :0 Mean :32.13 Mean :17.14 Mean :0.1163
## 11 :0 3rd Qu.:32.13 3rd Qu.:17.14 3rd Qu.:0.1163
## 12 :0 Max. :32.13 Max. :17.14 Max. :0.1163
## (Other):0
## Flav
## Min. :1.887
## 1st Qu.:1.887
## Median :1.887
## Mean :1.887
## 3rd Qu.:1.887
## Max. :1.887
##
## Row Chl NBI Anth
## 1 : 1 Min. :29.19 Min. :14.38 Min. :0.07868
## 10 : 1 1st Qu.:32.02 1st Qu.:16.44 1st Qu.:0.08535
## 11 : 1 Median :34.38 Median :17.81 Median :0.09354
## 14 : 1 Mean :34.97 Mean :18.12 Mean :0.09677
## 15 : 1 3rd Qu.:37.05 3rd Qu.:19.46 3rd Qu.:0.10917
## 16 : 1 Max. :43.89 Max. :23.16 Max. :0.12239
## (Other):33
## Flav
## Min. :1.820
## 1st Qu.:1.931
## Median :1.974
## Mean :1.973
## 3rd Qu.:2.037
## Max. :2.092
##
## Row Chl NBI Anth
## 12 :1 Min. :31.33 Min. :16.99 Min. :0.08183
## 13 :1 1st Qu.:34.20 1st Qu.:17.74 1st Qu.:0.09326
## 23 :1 Median :35.08 Median :18.29 Median :0.09747
## 32 :1 Mean :35.09 Mean :18.48 Mean :0.09702
## 41 :1 3rd Qu.:36.36 3rd Qu.:19.10 3rd Qu.:0.10265
## 45 :1 Max. :38.42 Max. :20.71 Max. :0.10721
## (Other):4
## Flav
## Min. :1.839
## 1st Qu.:1.898
## Median :1.944
## Mean :1.941
## 3rd Qu.:1.995
## Max. :2.032
##
## [1] 0.0001562666
## [1] 6.039853e-05
## [1] 14.76679
## [1] 4.10476
## [1] 0.004848629
## [1] 0.004125519
## [1] 5.602987
## [1] 1.155704
##
## Welch Two Sample t-test
##
## data: R4$Anth and R3$Anth
## t = 0.081174, df = 22.551, p-value = 0.936
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006306855 0.006821440
## sample estimates:
## mean of x mean of y
## 0.09702315 0.09676586
##
## Welch Two Sample t-test
##
## data: R4$Chl and R3$Chl
## t = 0.13309, df = 27.683, p-value = 0.8951
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.702350 1.938809
## sample estimates:
## mean of x mean of y
## 35.08876 34.97053
##
## Welch Two Sample t-test
##
## data: R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08156262 0.01725693
## sample estimates:
## mean of x mean of y
## 1.941185 1.973338
##
## Welch Two Sample t-test
##
## data: R4$Flav and R3$Flav
## t = -1.3877, df = 14.921, p-value = 0.1856
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08156262 0.01725693
## sample estimates:
## mean of x mean of y
## 1.941185 1.973338
There appears to be no signficant differences between Rows with 3 trees and rows with 4 trees for any of the measures.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 0.2346 0.0047878 2.5339 4.261e-08 ***
## Residuals 1742 3.2914 0.0018895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 21394 436.61 1.9533 0.0001046 ***
## Residuals 1742 389370 223.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 8.215 0.167646 2.865 3.11e-10 ***
## Residuals 1742 101.933 0.058515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 7866 160.527 2.325 8.14e-07 ***
## Residuals 1742 120273 69.043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # A tibble: 4 x 5
## Column Chl NBI Anth Flav
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 33.8 17.2 0.0945 2.01
## 2 2 35.4 18.6 0.0992 1.94
## 3 3 36.0 18.7 0.0990 1.96
## 4 4 34.3 18.7 0.0912 1.88
## 0 1 2 3 4
## 0 592 556 539 105
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 0.0122 0.0040655 2.0687 0.1024
## Residuals 1788 3.5138 0.0019652
##
## Call:
## lm(formula = F1$Anth ~ F1$Column)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09824 -0.02824 -0.00049 0.02676 0.38402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.094490 0.001822 51.861 <2e-16 ***
## F1$Column2 0.004753 0.002618 1.815 0.0696 .
## F1$Column3 0.004486 0.002639 1.700 0.0894 .
## F1$Column4 -0.003337 0.004694 -0.711 0.4772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04433 on 1788 degrees of freedom
## Multiple R-squared: 0.003459, Adjusted R-squared: 0.001787
## F-statistic: 2.069 on 3 and 1788 DF, p-value: 0.1024
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 1586 528.66 2.3101 0.07453 .
## Residuals 1788 409178 228.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = F1$Chl ~ F1$Column)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.263 -10.815 1.102 11.773 26.109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.7951 0.6217 54.355 <2e-16 ***
## F1$Column2 1.5975 0.8934 1.788 0.0739 .
## F1$Column3 2.2490 0.9006 2.497 0.0126 *
## F1$Column4 0.4924 1.6019 0.307 0.7586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.13 on 1788 degrees of freedom
## Multiple R-squared: 0.003861, Adjusted R-squared: 0.00219
## F-statistic: 2.31 on 3 and 1788 DF, p-value: 0.07453
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 2.305 0.76823 12.737 3.087e-08 ***
## Residuals 1788 107.843 0.06031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = F1$Flav ~ F1$Column)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90465 -0.15390 0.00885 0.16468 0.85068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.01032 0.01009 199.166 < 2e-16 ***
## F1$Column2 -0.06592 0.01450 -4.545 5.87e-06 ***
## F1$Column3 -0.04967 0.01462 -3.397 0.000695 ***
## F1$Column4 -0.13528 0.02601 -5.202 2.20e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2456 on 1788 degrees of freedom
## Multiple R-squared: 0.02092, Adjusted R-squared: 0.01928
## F-statistic: 12.74 on 3 and 1788 DF, p-value: 3.087e-08
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Column 3 821 273.626 3.8427 0.009333 **
## Residuals 1788 127318 71.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = F1$NBI ~ F1$Column)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.560 -6.118 0.291 6.107 31.944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.2255 0.3468 49.667 < 2e-16 ***
## F1$Column2 1.3478 0.4983 2.705 0.00690 **
## F1$Column3 1.5184 0.5024 3.022 0.00254 **
## F1$Column4 1.4342 0.8936 1.605 0.10866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.438 on 1788 degrees of freedom
## Multiple R-squared: 0.006406, Adjusted R-squared: 0.004739
## F-statistic: 3.843 on 3 and 1788 DF, p-value: 0.009333
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.05255 0.0262759 14.9537 3.788e-07 ***
## F1$Column 3 0.01228 0.0040942 2.3300 0.0727244 .
## F1$Row 49 0.23127 0.0047199 2.6861 6.175e-09 ***
## F1$Height:F1$Column 6 0.02055 0.0034247 1.9490 0.0699958 .
## F1$Height:F1$Row 98 0.20024 0.0020433 1.1628 0.1394606
## F1$Column:F1$Row 106 0.28473 0.0026861 1.5287 0.0006952 ***
## F1$Height:F1$Column:F1$Row 212 0.41375 0.0019517 1.1107 0.1490621
## Residuals 1315 2.31066 0.0017572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.05255 0.0262759 14.2135 7.586e-07 ***
## F1$Row 49 0.23404 0.0047763 2.5837 2.178e-08 ***
## F1$Height:F1$Row 98 0.20394 0.0020810 1.1257 0.1942
## Residuals 1642 3.03551 0.0018487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 2.0422 0.130152
## F1$Column 3 1577 525.57 2.5178 0.056677 .
## F1$Row 49 21813 445.16 2.1326 1.244e-05 ***
## F1$Height:F1$Column 6 1282 213.61 1.0233 0.408244
## F1$Height:F1$Row 98 27033 275.85 1.3215 0.022708 *
## F1$Column:F1$Row 106 24037 226.77 1.0863 0.265386
## F1$Height:F1$Column:F1$Row 212 59670 281.46 1.3484 0.001427 **
## Residuals 1315 274499 208.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 853 426.30 1.9372 0.14444
## F1$Row 49 21279 434.27 1.9734 8.34e-05 ***
## F1$Height:F1$Row 98 27286 278.43 1.2652 0.04464 *
## Residuals 1642 361346 220.06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 21394 436.61 1.9533 0.0001046 ***
## Residuals 1742 389370 223.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 0.915 0.45758 9.3660 9.143e-05 ***
## F1$Column 3 2.323 0.77447 15.8523 3.957e-10 ***
## F1$Row 49 8.139 0.16611 3.4001 1.213e-13 ***
## F1$Height:F1$Column 6 1.004 0.16728 3.4240 0.002334 **
## F1$Height:F1$Row 98 5.743 0.05860 1.1995 0.096365 .
## F1$Column:F1$Row 106 14.790 0.13953 2.8559 < 2.2e-16 ***
## F1$Height:F1$Column:F1$Row 212 12.988 0.06126 1.2540 0.012358 *
## Residuals 1315 64.245 0.04886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Height 2 100 50.044 0.7850 0.4563286
## F1$Column 3 818 272.802 4.2793 0.0051365 **
## F1$Row 49 7816 159.511 2.5021 8.551e-08 ***
## F1$Height:F1$Column 6 792 132.004 2.0707 0.0539220 .
## F1$Height:F1$Row 98 8175 83.418 1.3085 0.0268723 *
## F1$Column:F1$Row 106 7765 73.250 1.1490 0.1510179
## F1$Height:F1$Column:F1$Row 212 18842 88.877 1.3942 0.0004396 ***
## Residuals 1315 83831 63.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: F1$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## F1$Row 49 7866 160.527 2.3456 6.383e-07 ***
## F1$Column 3 789 263.021 3.8432 0.00934 **
## F1$Row:F1$Column 106 7724 72.864 1.0647 0.31303
## Residuals 1633 111761 68.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Dat = F1
#Identifying outliers outside
OBA <-boxplot(Dat$Anth,range = 3.5, plot = F)$out
OBC <-boxplot(Dat$Chl,range = 3, plot = F)$out
OBF <-boxplot(Dat$Flav,range = 3, plot = F)$out
OBN <-boxplot(Dat$NBI,range = 3,plot=F)$out
#Isolating outliers exceeding
OA = Dat[which(Dat$Anth %in% OBA),];OA
## Collection.Day Allocation Block Column Row group Group.ID Tree.ID Rep.
## 605 3 F1 34 3 17 38 116 IN4FM N
## 694 2 F1 10 2 20 33 69 IN4E4 N
## measure Height Flower Chl Flav Anth NBI
## 605 11 H N 59.122 1.640 0.483 36.05
## 694 7 M N 18.511 1.401 0.370 13.21
OC = Dat[which(Dat$Anth %in% OBC),];OC
## [1] Collection.Day Allocation Block Column
## [5] Row group Group.ID Tree.ID
## [9] Rep. measure Height Flower
## [13] Chl Flav Anth NBI
## <0 rows> (or 0-length row.names)
OF = Dat[which(Dat$Anth %in% OBF),];OF
## [1] Collection.Day Allocation Block Column
## [5] Row group Group.ID Tree.ID
## [9] Rep. measure Height Flower
## [13] Chl Flav Anth NBI
## <0 rows> (or 0-length row.names)
ON = Dat[which(Dat$Anth %in% OBN),];ON
## [1] Collection.Day Allocation Block Column
## [5] Row group Group.ID Tree.ID
## [9] Rep. measure Height Flower
## [13] Chl Flav Anth NBI
## <0 rows> (or 0-length row.names)
Dat <- Dat[-which(Dat$Anth %in% OBA),]
DATG <- ggplot(F1) + aes(x = Tree.ID, y = Anth,) + geom_boxplot(fill = "dodgerblue1", size = 1.25) + ylab("Anth") + xlab("Tree ID") + ggtitle("Anthocyanin Raw Data") +stat_summary(fun.y=mean, colour="gray90", geom="point", shape=18, size=3) +theme_bw()+theme(plot.title = element_text(hjust = 0.5))+geom_hline(yintercept = mean(F1$Anth))
DATG2 <- ggplot(Dat) + aes(x = Tree.ID, y = Anth,) + geom_boxplot(fill = "dodgerblue1", size = 1.25) + ylab("Anth") + xlab("Tree ID") + ggtitle("Anthocyanin Outliers Removed") +stat_summary(fun.y=mean, colour="gray90", geom="point", shape=18, size=3) +theme_bw()+theme(plot.title = element_text(hjust = 0.5))+geom_hline(yintercept = mean(Dat$Anth))
grid.arrange(DATG,DATG2)
#Total Mean
Dat$ATmean = mean(Dat$Anth)
Dat$CTmean = mean(Dat$Chl)
Dat$FTmean = mean(Dat$Flav)
Dat$NTmean = mean(Dat$NBI)
#Remove Total Mean
Dat$Anth2 = Dat$Anth - Dat$ATmean
Dat$Chl2 = Dat$Chl - Dat$CTmean
Dat$Flav2 = Dat$Flav - Dat$FTmean
Dat$NBI2 = Dat$NBI - Dat$NTmean
#Height Mean for Anth and Flav
DatH = group_by(Dat, Height)
DatH = summarise(DatH, HAnth = mean(Anth, na.rm = T), HChl = mean(Chl, na.rm =T), HFlav = mean(Flav,na.rm = T), HNBI = mean(NBI, na.rm = T))
#Add Height Mean to Data
Dat =merge(Dat, DatH, by.x = "Height")
#Calculate Height Mean Deviation from Total Mean
Dat$ATH = Dat$ATmean - Dat$HAnth
Dat$CTH = Dat$CTmean - Dat$HChl
Dat$FTH = Dat$FTmean - Dat$HFlav
Dat$NTH = Dat$NTmean - Dat$HNBI
#Controlling for Height Anth and Flav
Dat$Anth3 = Dat$Anth2 + Dat$ATH
Dat$Chl3 = Dat$Chl2 + Dat$CTH
Dat$Flav3 = Dat$Flav2 + Dat$FTH
Dat$NBI3 = Dat$NBI2 + Dat$NTH
#Column Mean for Chl, Flav,NBI
DatC = group_by(Dat, Column)
DatC = summarise(DatC, CAnth = mean(Anth, na.rm = T), CChl = mean(Chl, na.rm = T) ,CFlav = mean(Flav,na.rm = T), CNBI = mean(NBI,na.rm = T))
#Add Column Mean to Data
Dat =merge(Dat, DatC, by.x = "Column")
#Calculate Column Mean Deviation from Total Mean
Dat$ATC = Dat$ATmean - Dat$CAnth
Dat$CTC = Dat$CTmean - Dat$CChl
Dat$FTC = Dat$FTmean - Dat$CFlav
Dat$NTC = Dat$NTmean - Dat$CNBI
#Controlling for Column Chl, Flav and NBI
Dat$Anth4 = Dat$Anth3 + Dat$ATC
Dat$Chl4 = Dat$Chl2 + Dat$CTC
Dat$Flav4 = Dat$Flav3 + Dat$FTC
Dat$NBI4 = Dat$NBI3 + Dat$NTC
#Row Mean for Anth, Chl, Flav and and NBI
DatR = group_by(Dat, Row)
DatR = summarise(DatR, RAnth = mean(Anth,na.rm = T),RChl = mean(Chl,na.rm = T), RFlav = mean(Flav,na.rm = T), RNBI = mean(NBI,na.rm = T))
#Add Row Mean to Data
Dat =merge(Dat, DatR, by.x = "Row")
#Calculate Row Mean Deviation from Total Mean
Dat$ATR = Dat$ATmean - Dat$RAnth
Dat$CTR = Dat$CTmean - Dat$RChl
Dat$FTR = Dat$FTmean - Dat$RFlav
Dat$NTR = Dat$NTmean - Dat$RNBI
#Controlling for Row in Anth, Chl, Flav and NBI
Dat$Anth5 = Dat$Anth4 + Dat$ATR
Dat$Chl5 = Dat$Chl4 + Dat$CTR
Dat$Flav5 = Dat$Flav4 + Dat$FTR
Dat$NBI5 = Dat$NBI4 + Dat$NTR
## Analysis of Variance Table
##
## Response: Dat$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 0.00000 0.0000013 0.0008 0.99918
## Dat$Column 3 0.00043 0.0001418 0.0869 0.96722
## Dat$Row 49 0.00058 0.0000118 0.0072 1.00000
## Dat$Height:Dat$Column 6 0.01972 0.0032863 2.0144 0.06088 .
## Dat$Height:Dat$Row 98 0.18437 0.0018813 1.1532 0.15291
## Dat$Column:Dat$Row 106 0.26715 0.0025203 1.5448 0.00052 ***
## Dat$Height:Dat$Column:Dat$Row 212 0.41022 0.0019350 1.1861 0.04567 *
## Residuals 1313 2.14207 0.0016314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Dat$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 791 395.31 1.8944 0.150825
## Dat$Column 3 197 65.56 0.3142 0.815149
## Dat$Row 49 156 3.19 0.0153 1.000000
## Dat$Height:Dat$Column 6 1277 212.78 1.0196 0.410662
## Dat$Height:Dat$Row 98 26884 274.33 1.3146 0.024850 *
## Dat$Column:Dat$Row 106 23892 225.40 1.0801 0.279036
## Dat$Height:Dat$Column:Dat$Row 212 59481 280.57 1.3445 0.001572 **
## Residuals 1313 273994 208.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Dat$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 0.001 0.000562 0.0115 0.988538
## Dat$Column 3 0.011 0.003615 0.0742 0.973871
## Dat$Row 49 0.214 0.004364 0.0895 1.000000
## Dat$Height:Dat$Column 6 1.000 0.166608 3.4182 0.002368 **
## Dat$Height:Dat$Row 98 5.708 0.058249 1.1951 0.100948
## Dat$Column:Dat$Row 106 14.742 0.139078 2.8534 < 2.2e-16 ***
## Dat$Height:Dat$Column:Dat$Row 212 12.986 0.061253 1.2567 0.011685 *
## Residuals 1313 63.997 0.048741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Dat$NBI5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Height 2 3 1.352 0.0212 0.978979
## Dat$Column 3 50 16.825 0.2644 0.851104
## Dat$Row 49 22 0.453 0.0071 1.000000
## Dat$Height:Dat$Column 6 795 132.483 2.0816 0.052659 .
## Dat$Height:Dat$Row 98 8157 83.239 1.3079 0.027100 *
## Dat$Column:Dat$Row 106 7730 72.925 1.1458 0.155856
## Dat$Height:Dat$Column:Dat$Row 212 18762 88.498 1.3905 0.000485 ***
## Residuals 1313 83565 63.644
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Comparing correlations between the 4 dualex measures
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Analysis of Variance Table
##
## Response: Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## Chl5 1 0.32495 0.32495 225.0996 < 2.2e-16 ***
## Flav5 1 0.06820 0.06820 47.2416 8.648e-12 ***
## NBI5 1 0.02278 0.02278 15.7770 7.410e-05 ***
## Chl5:Flav5 1 0.00951 0.00951 6.5852 0.01036 *
## Chl5:NBI5 1 0.02430 0.02430 16.8357 4.260e-05 ***
## Flav5:NBI5 1 0.00218 0.00218 1.5087 0.21950
## Chl5:Flav5:NBI5 1 0.00016 0.00016 0.1093 0.74100
## Residuals 1782 2.57247 0.00144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth5 1 41543 41543 2.5419e+04 <2e-16 ***
## Flav5 1 202 202 1.2366e+02 <2e-16 ***
## NBI5 1 335250 335250 2.0513e+05 <2e-16 ***
## Anth5:Flav5 1 494 494 3.0218e+02 <2e-16 ***
## Anth5:NBI5 1 167 167 1.0217e+02 <2e-16 ***
## Flav5:NBI5 1 6102 6102 3.7336e+03 <2e-16 ***
## Anth5:Flav5:NBI5 1 0 0 1.5590e-01 0.693
## Residuals 1782 2912 2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth5 1 3.055 3.055 265.2320 < 2.2e-16 ***
## Chl5 1 0.056 0.056 4.8612 0.02759 *
## NBI5 1 74.121 74.121 6435.9193 < 2.2e-16 ***
## Anth5:Chl5 1 0.522 0.522 45.3622 2.203e-11 ***
## Anth5:NBI5 1 0.178 0.178 15.4884 8.620e-05 ***
## Chl5:NBI5 1 0.204 0.204 17.7366 2.664e-05 ***
## Anth5:Chl5:NBI5 1 0.000 0.000 0.0127 0.91035
## Residuals 1782 20.523 0.012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: NBI5
## Df Sum Sq Mean Sq F value Pr(>F)
## Anth5 1 13748 13748 2.0883e+04 <2e-16 ***
## Chl5 1 93493 93493 1.4201e+05 <2e-16 ***
## Flav5 1 9187 9187 1.3954e+04 <2e-16 ***
## Anth5:Chl5 1 49 49 7.4257e+01 <2e-16 ***
## Anth5:Flav5 1 103 103 1.5674e+02 <2e-16 ***
## Chl5:Flav5 1 1331 1331 2.0210e+03 <2e-16 ***
## Anth5:Chl5:Flav5 1 0 0 2.4100e-02 0.8768
## Residuals 1782 1173 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: Dat$Anth5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 0.00036 0.00036392 0.2152 0.6428
## Residuals 1788 3.02417 0.00169137
##
## Call:
## lm(formula = Dat$Anth5 ~ Dat$Flower)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.105618 -0.028027 -0.000984 0.025667 0.201307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0002807 0.0011451 -0.245 0.806
## Dat$FlowerY 0.0010050 0.0021665 0.464 0.643
##
## Residual standard error: 0.04113 on 1788 degrees of freedom
## Multiple R-squared: 0.0001203, Adjusted R-squared: -0.0004389
## F-statistic: 0.2152 on 1 and 1788 DF, p-value: 0.6428
## Analysis of Variance Table
##
## Response: Dat$Chl5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 19 18.847 0.0872 0.7679
## Residuals 1788 386652 216.248
##
## Call:
## lm(formula = Dat$Chl5 ~ Dat$Flower)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.009 -10.634 1.455 11.317 31.370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06388 0.40943 -0.156 0.876
## Dat$FlowerY 0.22870 0.77468 0.295 0.768
##
## Residual standard error: 14.71 on 1788 degrees of freedom
## Multiple R-squared: 4.874e-05, Adjusted R-squared: -0.0005105
## F-statistic: 0.08716 on 1 and 1788 DF, p-value: 0.7679
## Analysis of Variance Table
##
## Response: Dat$Flav5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 0.245 0.244694 4.4456 0.03513 *
## Residuals 1788 98.414 0.055042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = Dat$Flav5 ~ Dat$Flower)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86910 -0.14463 0.01577 0.15365 0.80773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.007279 0.006532 -1.114 0.2653
## Dat$FlowerY 0.026059 0.012359 2.108 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2346 on 1788 degrees of freedom
## Multiple R-squared: 0.00248, Adjusted R-squared: 0.001922
## F-statistic: 4.446 on 1 and 1788 DF, p-value: 0.03513
## Analysis of Variance Table
##
## Response: Dat$NBI5
## Df Sum Sq Mean Sq F value Pr(>F)
## Dat$Flower 1 13 13.012 0.1954 0.6585
## Residuals 1788 119071 66.595
##
## Call:
## lm(formula = Dat$NBI5 ~ Dat$Flower)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.4419 -5.8719 0.2175 5.8066 28.8506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05308 0.22721 0.234 0.815
## Dat$FlowerY -0.19003 0.42990 -0.442 0.659
##
## Residual standard error: 8.161 on 1788 degrees of freedom
## Multiple R-squared: 0.0001093, Adjusted R-squared: -0.00045
## F-statistic: 0.1954 on 1 and 1788 DF, p-value: 0.6585
## Warning: NAs introduced by coercion
## Analysis of Variance Table
##
## Response: H089$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Diff 1 0.000292 0.00029205 1.0337 0.3109
## Residuals 149 0.042094 0.00028251
## Analysis of Variance Table
##
## Response: H089$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height08 1 0.001142 0.00114216 4.1262 0.044 *
## Residuals 149 0.041244 0.00027681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$Anth
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height09 1 0.001706 0.00170619 6.2493 0.01351 *
## Residuals 149 0.040680 0.00027302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Diff 1 0.9 0.9142 0.0332 0.8556
## Residuals 149 4099.2 27.5113
## Analysis of Variance Table
##
## Response: H089$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height08 1 201.5 201.535 7.7025 0.006222 **
## Residuals 149 3898.6 26.165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$Chl
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height09 1 177.8 177.810 6.7547 0.01029 *
## Residuals 149 3922.3 26.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Diff 1 0.03732 0.037322 4.3811 0.03803 *
## Residuals 149 1.26932 0.008519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height08 1 0.01095 0.0109540 1.2597 0.2635
## Residuals 149 1.29569 0.0086959
## Analysis of Variance Table
##
## Response: H089$Flav
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height09 1 0.00096 0.0009574 0.1093 0.7415
## Residuals 149 1.30568 0.0087630
## Analysis of Variance Table
##
## Response: H089$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Diff 1 6.38 6.3824 0.6957 0.4056
## Residuals 149 1366.93 9.1740
## Analysis of Variance Table
##
## Response: H089$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height08 1 29.09 29.0855 3.224 0.07459 .
## Residuals 149 1344.22 9.0216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H089$NBI
## Df Sum Sq Mean Sq F value Pr(>F)
## H089$Height09 1 41.76 41.757 4.6726 0.03224 *
## Residuals 149 1331.55 8.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: H0892$Height08
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 48775 995.40 1.9908 0.001269 **
## Residuals 121 60501 500.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Height09
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 62417 1273.82 2.0535 0.0007953 ***
## Residuals 121 75058 620.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Diff
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 12742 260.04 0.7015 0.9197
## Residuals 121 44853 370.69
## Analysis of Variance Table
##
## Response: H0892$Height08
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Column 3 30712 10237.3 21.761 6.033e-12 ***
## Residuals 167 78564 470.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Height09
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Column 3 49883 16627.8 31.702 2.842e-16 ***
## Residuals 167 87592 524.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Diff
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Column 3 3480 1159.90 3.5794 0.01519 *
## Residuals 167 54115 324.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Analysis of Variance Table
##
## Response: H0892$Height08
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 48775 995.40 1.9908 0.001269 **
## Residuals 121 60501 500.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Height09
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 62417 1273.82 2.0535 0.0007953 ***
## Residuals 121 75058 620.32
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: H0892$Diff
## Df Sum Sq Mean Sq F value Pr(>F)
## H0892$Row 49 12742 260.04 0.7015 0.9197
## Residuals 121 44853 370.69
## Analysis of Variance Table
##
## Response: HData$F8
## Df Sum Sq Mean Sq F value Pr(>F)
## HData$Column 3 2427 809.10 3.181 0.02546 *
## Residuals 167 42477 254.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: HData$F9
## Df Sum Sq Mean Sq F value Pr(>F)
## HData$Column 3 3731 1243.57 4.7497 0.003321 **
## Residuals 167 43724 261.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Variance Table
##
## Response: HData$FDiff
## Df Sum Sq Mean Sq F value Pr(>F)
## HData$Column 3 150 50.054 0.1985 0.8973
## Residuals 167 42111 252.161